• 대한수의사회지
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• 제44권5호
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• pp.396-399
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• 2008

• 대한수의사회지
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• 제50권2호
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• pp.86-89
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• 2014

• 보건행정학회지
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• 제33권4호
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• pp.479-488
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• 2023

Background: By applying the suggested criteria for needs-based chronic medical care and long-term care delivery system for the elderly, the current status of delivery system was identified and regional delivery systems were categorized according to quantity and quality of delivery system. Methods: National claims data were used for this study. All claims data of medical and long-term care uses by the elderly and all claims data from long-term care hospitals and nursing homes in 2016 were analyzed to categorize the regional medical and long-term care delivery system. The current status of the delivery system with a high possibility of transition to a needs-based appropriate delivery system was identified. The necessary and actual amount of regional supply was calculated based on their needs, and the structure of delivery systems was evaluated in terms of the needs-based quality of the system. Finally, all regions were categorized into 15 types of medical and care delivery systems for the elderly. Results: Of the total 55 regions, 89.1% of regions had an oversupply of elderly medical and care services compared to the necessary supply based on their needs. However, 69.1% of regions met the criteria for less than two types of needs groups, and 21.8% of regions were identified as regions where the numbers of institutions or regions with a high possibility of transition to an appropriate delivery system were below the average levels for all four needs groups. Conclusion: In order to establish an appropriate community-based integrated elderly care system, it is necessary to analyze the characteristics of the regional delivery system categories and to plan a needs-based delivery system regionally.

• 대한영상의학회지
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• 제85권4호
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• pp.693-704
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• 2024

흉부 X선은 인공지능 기술이 활발히 적용되고 있는 대표적인 영상 검사이다. 흉부 X선 영상에서 다양한 이상 소견을 자동으로 검출하여 의사의 판독을 보조하는 인공지능 기반 소프트웨어 의료기기들이 국내에서 시판되고 있고, 임상 적용이 활발히 이루어지고 있다. 이러한 흉부 X선 인공지능 검출 보조 의료기기의 임상 도입에 있어, 도입 전 성능 및 유효성 평가, 적용 대상, 분석 결과 제공의 대상 및 방식, 도입 후 모니터링, 법적 책임 문제 등 다양한 현실적인 사항에 대한 고려가 필요하고, 각 의료기관의 상황에 따른 적절한 의사결정이 필요하다. 인공지능 검출 보조 의료기기의 안전하고 효율적인 도입 및 운영을 위해서는 전문 지식을 갖춘 영상의학과 전문의의 적극적인 역할이 필수적이다.

• Radiation Oncology Journal
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• 제17권3호
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• pp.203-208
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• 1999

목적 : 폐암 환자의 대부분은 진단 당시 수술을 받을 수 없는 병기로 발견되기 때문에 수술 조직이 아닌 기관지내시경 조직에서 자발성 아포토시스 정도를 평가하여 이의 임상적 의의에 대한 기초 자료로 삼고자 본 연구를 시행하였다. 대상 및 방법 : 1990년 9월부터 1994년 9월까지의 4년동안 흉부에 방사선치료를 받은 폐 편평세포암 환자 중 조직표본이 충분히 보관되어 있으며 추적이 가능하였던 19명을 대상으로 하였다. 병기는 II기가 1명, IIIa기 8명, IIIb기 5명, IV기 5명이었다. 면역조직화학적 염색법으로 자발성 아포토시스율(Al)과 p53 단백질 양성률을 관찰하였다. 결과 : 19명 중 16명은 $5\~15$ 개월 후에 사망하였으며 3명은 55, 67, 67 개월간 생존하고 있다. 중앙생존기간은 17개월, 평균 생존기간은 24 개월이었다. AI는 $0\~1\%$의 범위로 중앙값이 $0.4\%$였다. AI가 낮은 군에서 진단 당시 원격전이가 있었던 경우가 $50\%$ (5/10) 였고, 높은 근에서는 원격전이가 전혀 없었다(0/9). 생존기간에 영향을 줄 수 있는 예후인자들의 분석 결과 단변량 분석에서는 M병기가 통계학적으로 유의한 차이를 보였고, 다변량 분석에서는 AI, 화학요법, M병기, T병기, 병기가 의의가 있었다. 자발성 아포토시스와 p53 변이 사이의 관련은 관찰되지 않았다. 결론 : AI는 진단 당시 원격전이와 관련이 있으며, p53 변이와는 관련이 없었다. AI가 낮은 군에서 높은 군보다 생존기간이 짧은 경향을 보였다.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• 대한수의사회지
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• 제47권9호
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• pp.860-863
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• 2011

최근 양돈협회 자료에 의하면 FMD가 발생하였으나 임상증상이 발현된 돼지만을 부분적으로 살처분한 양돈장이 전국적으로 558호에 약 83만두 규모로 알려져 있다. 또한 앞으로도 환절기나 동절기에 FMD가 재발할 가능성이 있다. 그렇다면 부분 살처분을 실시한 농장은 FMD증상이 어떻게 변화하고 음성화는 가능한 것일까? 기존의 부분 살처분 농장은 FMD바이러스가 농장 내 상존하여 지속적이고 반복적으로 피해를 주지 않을까? FMD가 발생하여 부분 살처분 된 농장이 하루빨리 안정화되고 음성화를 이루기 위해 어떤 조치가 필요한 것인가에 대해 합리적인 대책이 요구된다. 다음의 사례는 AI센터에서 FMD가 발생한 후 음성화 되기 까지 과정을 요약 보고한 자료이다. FMD 음성화에 작은 밑거름이 되었으면 한다.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.73-76
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• 2023

We introduce DCNN and DRAE appraoches for compression of medical videos, in order to decrease file size and storage requirements, there is an increasing need for medical video compression nowadays. Using a lossy compression technique, a higher compression ratio can be attained, but information will be lost and possible diagnostic mistakes may follow. The requirement to store medical video in lossless format results from this. The aim of utilizing a lossless compression tool is to maximize compression because the traditional lossless compression technique yields a poor compression ratio. The temporal and spatial redundancy seen in video sequences can be successfully utilized by the proposed DCNN and DRAE encoding. This paper describes the lossless encoding mode and shows how a compression ratio greater than 2 (2:1) can be achieved.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.557-560
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• 2023

In this paper, we introduce a pre-training method leveraging the capabilities of the Vision Transformer (ViT) for disease diagnosis in conventional Fundus images. Recognizing the need for effective representation learning in medical images, our method combines the Vision Transformer with a Masked Autoencoder to generate meaningful and pertinent image augmentations. During pre-training, the Masked Autoencoder produces an altered version of the original image, which serves as a positive pair. The Vision Transformer then employs contrastive learning techniques with this image pair to refine its weight parameters. Our experiments demonstrate that this dual-model approach harnesses the strengths of both the ViT and the Masked Autoencoder, resulting in robust and clinically relevant feature embeddings. Preliminary results suggest significant improvements in diagnostic accuracy, underscoring the potential of our methodology in enhancing automated disease diagnosis in fundus imaging.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.